Power/Performance Bits: May 11

Light-emitting silicon
Researchers from the Eindhoven University of Technology, Friedrich-Schiller-Universität Jena, Johannes Kepler University, and Technische Universität München developed a silicon germanium alloy that can emit light, paving the way for a silicon laser that could be integrated for on-chip and chip-to-chip communication.

Bulk silicon is extremely inefficient at emitting light, leading to the use of semiconductors like gallium arsenide and indium phosphide. These, however, are more expensive than silicon and difficult to integrate.

To create a silicon-compatible laser, the researchers combined silicon and germanium in a hexagonal structure that is able to emit light.

“The crux is in the nature of the so-called band gap of a semiconductor,” said lead researcher Erik Bakkers from Eindhoven University of Technology (TU/e). “If an electron ‘drops’ from the conduction band to the valence band, a semiconductor emits a photon: light.” But if the conduction band and valence band are displaced with respect to each other, which is called an indirect band gap, no photons can be emitted, which is the case in silicon. “A 50-year old theory showed however that silicon, alloyed with germanium, shaped in a hexagonal structure does have a direct band gap, and therefore potentially could emit light,” said Bakkers.

Growing silicon in a hexagonal structure is difficult. The researchers fist investigated hexagonal silicon while making nanowires. They first grew nanowires made from another material, with a hexagonal crystal structure. Then they grew a silicon-germanium shell on this template. Elham Fadaly, of TU/e and shared first author, explained, “We were able to do this such that the silicon atoms are built on the hexagonal template, and by this forced the silicon atoms to grow in the hexagonal structure.”

The team managed to increase the quality of the hexagonal silicon-germanium shells by reducing the number of impurities and crystal defects. When exciting the nanowire with a laser, they could measure the efficiency of the new material. Alain Dijkstra of TU/e and shared first author said, “Our experiments showed that the material has the right structure, and that it is free of defects. It emits light very efficiently.”

Bakkers thinks that creating a laser is now a matter of time. “By now we have realized optical properties which are almost comparable to indium phosphide and gallium arsenide, and the materials quality is steeply improving. If things run smoothly, we can create a silicon-based laser in 2020. This would enable a tight integration of optical functionality in the dominant electronics platform, which would break open prospects for on-chip optical communication and affordable chemical sensors based on spectroscopy.”

The team is working on creating such a laser as well as investigating how to integrate the hexagonal silicon in cubic silicon microelectronics.

AI for battery life
Researchers from the University of Cambridge and Newcastle University designed a machine learning method that can predict battery health with greater accuracy than standard industry methods.

The Gaussian process battery monitoring system sends electrical pulses to the battery and monitors its response. A machine learning model is then used to discover specific features in the electrical response that are the tell-tale sign of battery aging. The researchers performed over 20,000 experimental measurements to train the model, which they say is the largest dataset of its kind.

The team’s Gaussian process model takes the entire spectrum as input, without further feature engineering, and automatically determines which spectral features predict degradation. They say the model accurately predicts the remaining useful life, even without complete knowledge of past operating conditions of the battery.

The model learns how to distinguish important signals from irrelevant noise. The method is non-invasive and is a simple add-on to any existing battery systems, according to the team.

“Safety and reliability are the most important design criteria as we develop batteries that can pack a lot of energy in a small space,” said Alpha Lee of Cambridge’s Cavendish Laboratory, who co-led the research. “By improving the software that monitors charging and discharging, and using data-driven software to control the charging process, I believe we can power a big improvement in battery performance.”

The machine learning model can also be interpreted to give hints about the physical mechanism of degradation. The model can inform which electrical signals are most correlated with aging, which in turn allows them to design specific experiments to probe why and how batteries degrade.

“Machine learning complements and augments physical understanding,” said co-first author Yunwei Zhang, also from the Cavendish Laboratory. “The interpretable signals identified by our machine learning model are a starting point for future theoretical and experimental studies.”

The researchers are now using their machine learning platform to understand degradation in different battery chemistries. They are also developing optimal battery charging protocols that utilize machine learning to enable fast charging and minimize degradation.

Organic proton battery
Researchers at Uppsala University developed an all-organic proton battery that can be charged quickly and maintain capacity over 500 charges without any significant loss.

“I’m sure that many people are aware that the performance of standard batteries declines at low temperatures. We have demonstrated that this organic proton battery retains properties such as capacity down to as low as -24°C,” said Christian Strietzel of Uppsala University’s Department of Materials Science and Engineering.

“The point of departure for our research has therefore been to develop a battery built from elements commonly found in nature and that can be used to create organic battery materials,” said Strietzel.

The research team chose quinones as the active material in their battery. These organic carbon compounds are plentiful in nature and occur during photosynthesis. The characteristic of quinones that the researchers utilized is their ability to absorb or emit hydrogen ions, which only contain protons, during charging and discharging. An acidic aqueous solution was used as an electrolyte.

The team said it provides a safe battery free from the hazard of explosion or fire and is more environmentally friendly. “There remains a great deal of further development to be done on the battery before it becomes a household item; however, the proton battery we have developed is a large stride towards being able to manufacture sustainable organic batteries in the future,” said Strietzel.